(340f) Quantum Molecular Sequencing: Unravelling Genomic Information One Molecule at a Time
- Conference: AIChE Annual Meeting
- Year: 2017
- Proceeding: 2017 Annual Meeting
- Group: Topical Conference: Chemical Engineers in Medicine
- Time: Tuesday, October 31, 2017 - 2:35pm-3:00pm
Nanoelectronic DNA sequencing can provide an important alternative to sequencing-by-synthesis by reducing sample preparation time, cost, and complexity as a high-throughput next-generation technique with accurate single-molecule identification. However, sample noise and signature overlap continue to prevent high-resolution and accurate sequencing results. Probing the molecular orbitals of chemically distinct DNA nucleobases offers a path for facile sequence identification, but molecular entropy (from nucleotide conformations) makes such identification difficult when relying only on the energies of lowest-unoccupied and highest-occupied molecular orbitals (LUMO and HOMO). In this talk I will present recent results on Quantum Molecular Sequencing, which utilizes nanoelectronic spectroscopy to develop a molecular recognition technique, using new biophysical parameters are developed to better characterize molecular orbitals of individual nucleobases, intended for single-molecule DNA sequencing using quantum tunneling of charges. For this analysis, theoretical models for quantum tunneling are combined with transition voltage spectroscopy to obtain measurable parameters unique to the molecule within an electronic junction. Scanning tunneling spectroscopy is then used to measure these nine biophysical parameters for DNA nucleotides, and a modified machine learning algorithm identified nucleobases. The new parameters significantly improve base calling over merely using LUMO and HOMO frontier orbital energies. Furthermore, high accuracies for identifying DNA nucleobases were observed at different pH conditions. Besides better characterization of molecular orbitals using the new biophysical parameters and specific biochemical conditions, Quantum Molecular Sequencing combines new algorithms and customized machine learning techniques for sequencing single-molecule DNA, RNA, epigenetic modifications, and RNA structure, using a simple comparison of QM-Seq electronic spectra with a library of respective signatures without any prior expectation. These results have significant implications for developing a robust and accurate high-throughput multiomics technique.